ArticleSensors (Basel, Switzerland)2019
A Novel Framework with High Diagnostic Sensitivity for Lung Cancer Detection by Electronic Nose.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring Components, Sensors, and Techniques for Cancer Detection via eNose Technology: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Article
- Enhanced Lung Cancer Classification Accuracy via Hybrid Sensor Integration and Optimized Fuzzy Logic-Based Electronic Nose.Sensors (Basel, Switzerland) · 2025Article
- The electronic nose in lung cancer diagnostics: a systematic review and meta-analysis.ERJ open research · 2025Article
- Electronic Nose Drift Suppression Based on Smooth Conditional Domain Adversarial Networks.Sensors (Basel, Switzerland) · 2024Article
- Volatile Organic Compound Assessment as a Screening Tool for Early Detection of Gastrointestinal Diseases.Microorganisms · 2023Review
- A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The electronic nose (e-nose) system is a newly developing detection technology for its advantages of non-invasiveness, simple operation, and low cost. However, lung cancer screening through e-nose requires effective pattern recognition frameworks. Existing frameworks rely heavily on hand-crafted features and have relatively low diagnostic sensitivity. To handle these problems, gated recurrent unit based autoencoder (GRU-AE) is adopted to automatically extract features from temporal and high-dimensional e-nose data. Moreover, we propose a novel margin and sensitivity based ordering ensemble pruning (MSEP) model for effective classification. The proposed heuristic model aims to reduce missed diagnosis rate of lung cancer patients while maintaining a high rate of overall identification. In the experiments, five state-of-the-art classification models and two popular dimensionality reduction methods were involved for comparison to demonstrate the validity of the proposed GRU-AE-MSEP framework, through 214 collected breath samples measured by e-nose. Experimental results indicated that the proposed intelligent framework achieved high sensitivity of 94.22%, accuracy of 93.55%, and specificity of 92.80%, thereby providing a new practical means for wide disease screening by e-nose in medical scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.